Electrical management of intractable ventricular tachycardia via implantable antitachycardia devices has become a major form of therapy. There are many proposed methods for differentiating sinus rhythm from ventricular tachycardia (VT). Most early methods were based primarily on timing information which could be implemented in the existing hardware available in antitachycardia devices. Besides sustained high rate, simple measures derived from rate have been examined for more precise detection of ventricular tachycardia. These include the maximal rate of sinus tachycardia compared to the onset of VT, changes in rate at the onset of VT, and rate stability during VT.
Among the methods most widely used for detection of VT in single chamber antitachycardia devices are rate, rate stability, and sudden onset. These methods use some modern digital circuitry but are based on hardware trigger detection and ignore the information content of the intraventricular signal itself. An example of these methods is disclosed in U.S. Pat No. 3,857,398. U.S. Pat. No. 4,393,877 attempts to determine heart rate and includes a differentiator and a zero crossing detector connected in parallel to receive the ECG signal and other circuitry to develop a single count for each PQRST waveform and avoid overcounting.
Along with rate, morphology differences between ventricular electrograms during sinus rhythm and ventricular tachycardia are being investigated for more accurate discrimination. One commercially available device for treatment of VT uses rate alone or both rate and a probability density function (PDF) as an attempt to discriminate sinus rhythm from ventricular fibrillation. It has been less reliable in distinguishing VT from sinus rhythm. Such a device is disclosed in U.S. Pat. No. 4,184,493. Other U.S. patents which disclose various detection devices include U.S. Pat. Nos. 3,577,983; 3,616,791; 3,821,948; 3,861,387; 3,878,833; 3,902,479; 3,940,692; 4,170,992; 4,296,755 and 4,523,595.
Recently, investigators have proposed a variety of schemes for detection of VT based on analysis of the ventricular electrocardiogram. Amplitude distribution analysis, a software algorithm similar to PDF, has also been tested with limited success. Some success has been reported using a gradient pattern detection (GPD) method, described in a paper by D. W. Davies, R. J. Wainwright, M. A. Tooley, D. Lloyd, A. W. Nathan, R. A. J. Spurrell, and A. J. Camm, entitled "Detection of Pathological Tachycardia by Analysis of Electrogram Morphology", PACE, Vol. 9, pp. 200-208, Mar.-Apr. 1986, which proposes discrimination of ventricular electrograms during sinus rhythm from those during VT by using the order in which the first derivative of the ventricular depolarization crosses predetermined thresholds. Such crossings are directly dependent on the amplitude of the waveforms under analysis. Hence fluctuations in amplitude may cause ventricular depolarization with identical morphology to be classified differently. There is no performance measure in the GPD algorithm to determine how closely a waveform matches the template. Thus there appears to be no general means for setting thresholds to discriminate sinus rhythm depolarization from those during ventricular tachycardia using the GPD algorithm.
Another technique proposed for detecting VT combines bandpass filtering, rectifying, amplitude scaling, and signal integration over a 5-second moving time window. A feature extraction algorithm utilizing the product of the peak amplitude difference (maximum-minimum) and duration (time between maximum and minimum) has been presented, but has been tested on only four patients.
In other studies, the use of amplitude, dV/dt, and the -3 dB point of the frequency domain power spectrum have not been consistently successful in discriminating sinus rhythm from VT.
Another method, the area of difference method, demonstrated successful results in 10 patients. However, the results can be adversely affected by fluctuation in electrogram amplitude and baseline changes.
Recently, the reliability and robustness of correlation waveform analysis (CWA) has been shown for differentiating sinus rhythm from VT. CWA has the advantage of being independent of amplitude and baseline fluctuations, and produces an index of merit reflecting morphological changes only.
Correlation waveform analysis employs the correlation coefficient, .rho., a performance measure for analysis of similarity between a template and waveform under analysis. The correlation coefficient is independent of amplitude fluctuations, baseline changes, and produces an output between -1 and 1. Mathematically, the correction coefficient is defined as, ##EQU1## The correlation coefficient is equivalent to the following squared-error norm: ##EQU2## To avoid computing the square root, the performance measure is: EQU .rho.=sign(.rho.).rho..sup.2
where:
t.sub.i =the template points. PA1 s.sub.i =the signal points to be processed. PA1 .sub.t =the template average. PA1 .sub.s =the signal average. PA1 s.sub.i =the first derivative of the signal points. PA1 .phi.=the value of the performance measure.
The correlation coefficient has been shown to produce a reliable measure for recognition of waveform changes.